Abstract In the process of computer vision-based seam tracking, although strong noise interference exists such as that arising from arc and splash in the welding process, the tracking effect has been significantly improved by noise reduction, feature point probability estimation and other methods. However, in the process of automatic tracking of a seam tracking system, the robot's pose cannot be adapted to various welding conditions in real time, resulting in decreased weld quality. To enhance the adaptability and real-time estimation of the robot's pose during welding, this paper proposes a real-time pose estimation method for a seam tracking system. In a welding environment with strong noise interference, the real-time pose estimation of the welding workpiece is carried out, and the robot's pose is changed in real time. The pose estimation is realized by building point cloud data, constructing a tool coordinate system in real time and obtaining rotation angles. To accurately acquire the point cloud data, efficient convolution operators (ECO) for tracking and the morphological intersection method integrated with a support vector machine (SVM) are adopted to classify the images with strong noise to better suppress the tracking model drift. The offline tracking test shows that compared with the original tracking algorithm, the proposed method can significantly suppress the peak value of pixel error and reduce its mean value. The welding experiment results show that the proposed method can be adapted to various welding conditions and achieve adaptive and real-time robot pose goals, which improves the welding precision and quality.

中文翻译：

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一种用于接缝跟踪系统的实时位姿估计方法研究

摘要 在基于计算机视觉的焊缝跟踪过程中，虽然存在焊接过程中电弧、飞溅等强噪声干扰，但通过降噪、特征点概率估计等方法使跟踪效果得到显着提高。然而，在焊缝跟踪系统的自动跟踪过程中，机器人的姿态不能实时适应各种焊接条件，导致焊接质量下降。为提高焊接过程中机器人位姿的适应性和实时估计，本文提出了一种焊缝跟踪系统的实时位姿估计方法。在噪声干扰强的焊接环境中，对焊接工件进行实时位姿估计，实时改变机器人位姿。姿态估计是通过构建点云数据、实时构建工具坐标系和获取旋转角度来实现的。为了准确获取点云数据，采用高效卷积算子（ECO）进行跟踪，结合支持向量机（SVM）的形态交集方法对强噪声图像进行分类，以更好地抑制跟踪模型漂移。离线跟踪测试表明，与原始跟踪算法相比，该方法能够显着抑制像素误差的峰值，降低其均值。焊接实验结果表明，该方法能够适应各种焊接条件，实现机器人位姿自适应实时目标，提高了焊接精度和质量。